Page 22 - ES 2020-21_Volume-1-2 [28-01-21]
P. 22
Saving Lives and Livelihoods Amidst a Once-in-a-Century Crisis 5
of evidence, India’s policy response valuing human life, even while paying the price of
temporary GDP decline, has initiated the process of transformation where the short-term
trade-off between lives and livelihoods is converted into a win-win in the medium to long-
term that saves both lives and livelihoods.
Box 1: Flattening the Curve
Epidemiological research highlights that a key strategy to combat the spread of an epidemic is
termed as “flattening the curve.” The curve refers to the projected number of people who will
contract the disease in a given population. The shape of the curve varies according to the rapidity
with which the infection spreads in the community. There is a “peak” of the disease, where the
number of infected individuals reaches a maximum, followed by a decline. Policymakers care
particularly about the time taken to reach this peak because this determines the time available to
respond to early signs of a pandemic. The peak number of infected individuals is also important
as it determines the scale of medical facilities required. Overloaded healthcare systems that are
forced to operate beyond their capacity lead to higher case fatality rates. In the short run, the
capacity of any country’s health system is finite (number of hospital beds, number of skilled
health professionals, ventilators/Integrated Care Units among others). This puts an upper
bound on the number of patients that can be properly treated, at any given point of time. If the
spread of the pandemic exceeds the existing capacity of the health system, it may lead to higher
mortality rates. The ‘flattening of the curve’ spreads the pandemic over time, enabling more
people to receive proper health treatment – ultimately lowering the fatality rate.
Flattening the Curve
The transmission potential is often summarized by the expected number of new infections
caused by a typical infected individual during the early phase of the outbreak, and is usually
denoted by the basic reproduction number, R . It is simply the expected number of new
0
cases of the disease caused by a single individual. Three possibilities exist for the potential
transmission or decline of a disease, depending on its R value: (i) If R < 1, each existing
0
0
infection causes less than one new infection and the disease eventually peters out; (ii) If R
0
=1, each existing infection causes one new infection and will not lead to an outbreak or an
epidemic and (iii) If R > 1, each existing infection causes more than one new infection and
0
there may be an outbreak or epidemic. Occasionally, one person may transmit to tens or even
hundreds of other cases - this phenomenon is called super-spreading.